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Segmenting Patients and Physicians Using Preferences from Discrete Choice Experiments

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  • Ken Deal

Abstract

People often form groups or segments that have similar interests and needs and seek similar benefits from health providers. Health organizations need to understand whether the same health treatments, prevention programs, services, and products should be applied to everyone in the relevant population or whether different treatments need to be provided to each of several segments that are relatively homogeneous internally but heterogeneous among segments. Our objective was to explain the purposes, benefits, and methods of segmentation for health organizations, and to illustrate the process of segmenting health populations based on preference coefficients from a discrete choice conjoint experiment (DCE) using an example study of prevention of cyberbullying among university students. We followed a two-level procedure for investigating segmentation incorporating several methods for forming segments in Level 1 using DCE preference coefficients and testing their quality, reproducibility, and usability by health decision makers. Covariates (demographic, behavioral, lifestyle, and health state variables) were included in Level 2 to further evaluate quality and to support the scoring of large databases and developing typing tools for assigning those in the relevant population, but not in the sample, to the segments. Several segmentation solution candidates were found during the Level 1 analysis, and the relationship of the preference coefficients to the segments was investigated using predictive methods. Those segmentations were tested for their quality and reproducibility and three were found to be very close in quality. While one seemed better than others in the Level 1 analysis, another was very similar in quality and proved ultimately better in predicting segment membership using covariates in Level 2. The two segments in the final solution were profiled for attributes that would support the development and acceptance of cyberbullying prevention programs among university students. Those segments were very different—where one wanted substantial penalties against cyberbullies and were willing to devote time to a prevention program, while the other felt no need to be involved in prevention and wanted only minor penalties. Segmentation recognizes key differences in why patients and physicians prefer different health programs and treatments. A viable segmentation solution may lead to adapting prevention programs and treatments for each targeted segment and/or to educating and communicating to better inform those in each segment of the program/treatment benefits. Segment members’ revealed preferences showing behavioral changes provide the ultimate basis for evaluating the segmentation benefits to the health organization. Copyright Springer International Publishing Switzerland 2014

Suggested Citation

  • Ken Deal, 2014. "Segmenting Patients and Physicians Using Preferences from Discrete Choice Experiments," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 7(1), pages 5-21, March.
    • Handle: RePEc:spr:patien:v:7:y:2014:i:1:p:5-21
    DOI: 10.1007/s40271-013-0037-9
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    References listed on IDEAS

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    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    3. Francesca Bassi, 2007. "Latent class factor models for market segmentation: an application to pharmaceuticals," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(2), pages 279-287, August.
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    Cited by:

    1. Laura Graf & Marlen Rimbeck & Jutta Stumpf-Wollersheim & Isabell M. Welpe, 2024. "Academic success is in the eye of the beholder: understanding scholars’ implicit appointment preferences through adaptive choice-based conjoint analysis," Journal of Business Economics, Springer, vol. 94(5), pages 725-761, July.
    2. Enayatollah Homaie Rad & Mohammad Hajizadeh & Vahid Yazdi-Feyzabadi & Sajad Delavari & Zahra Mohtasham-Amiri, 2021. "How Much Money Should be Paid for a Patient to Isolate During the COVID-19 Outbreak? A Discrete Choice Experiment in Iran," Applied Health Economics and Health Policy, Springer, vol. 19(5), pages 709-719, September.

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